def apply_actlist(trace, model, parameters=None):
"""
Apply log-skeleton based conformance checking given the list of activities of a trace
and a log-skeleton model
Parameters
--------------
trace
List of activities of a trace
model
Log-skeleton model
parameters
Parameters of the algorithm, including:
- the activity key (pm4py:param:activity_key)
- the list of considered constraints (considered_constraints) among: equivalence, always_after, always_before, never_together, directly_follows, activ_freq
Returns
--------------
aligned_trace
Containing:
- is_fit => boolean that tells if the trace is perfectly fit according to the model
- dev_fitness => deviation based fitness (between 0 and 1; the more the trace is near to 1 the more fit is)
- deviations => list of deviations in the model
"""
if parameters is None:
parameters = {}
consid_constraints = exec_utils.get_param_value(
Parameters.CONSIDERED_CONSTRAINTS, parameters,
Parameters.DEFAULT_CONSIDERED_CONSTRAINTS.value)
trace_info = trace_skel.get_trace_info(trace)
ret = {}
ret[Outputs.DEVIATIONS.value] = []
dev_total = 0
conf_total = 0
default_considered_constraints = Parameters.DEFAULT_CONSIDERED_CONSTRAINTS.value
i = 0
while i < len(default_considered_constraints):
if default_considered_constraints[i] in consid_constraints:
if default_considered_constraints[
i] == DiscoveryOutputs.ACTIV_FREQ.value:
this_constraints = {
x: y
for x, y in model[
default_considered_constraints[i]].items()
}
conf_total += len(
list(act for act in trace_info[i]
if act in this_constraints)) + len(
list(act for act in trace_info[i]
if act not in this_constraints)) + len(
list(act for act in this_constraints
if min(this_constraints[act]) > 0
and not act in trace))
for act in trace_info[i]:
if act in this_constraints:
if trace_info[i][act] not in this_constraints[act]:
dev_total += 1
ret[Outputs.DEVIATIONS.value].append(
(default_considered_constraints[i],
(act, trace_info[i][act])))
else:
dev_total += 1
ret[Outputs.DEVIATIONS.value].append(
(default_considered_constraints[i], (act, 0)))
for act in this_constraints:
if min(this_constraints[act]) > 0 and not act in trace:
dev_total += 1
ret[Outputs.DEVIATIONS.value].append(
(default_considered_constraints[i],
(act, this_constraints[act])))
elif default_considered_constraints[
i] == DiscoveryOutputs.NEVER_TOGETHER.value:
this_constraints = {
x
for x in model[default_considered_constraints[i]]
if x[0] in trace
}
conf_total += len(this_constraints)
setinte = this_constraints.intersection(trace_info[i])
dev_total += len(setinte)
if len(setinte) > 0:
ret[Outputs.DEVIATIONS.value].append(
(default_considered_constraints[i], tuple(setinte)))
else:
this_constraints = {
x
for x in model[default_considered_constraints[i]]
if x[0] in trace
}
conf_total += len(this_constraints)
setdiff = this_constraints.difference(trace_info[i])
dev_total += len(setdiff)
if len(setdiff) > 0:
ret[Outputs.DEVIATIONS.value].append(
(default_considered_constraints[i], tuple(setdiff)))
i = i + 1
ret[Outputs.NO_DEV_TOTAL.value] = dev_total
ret[Outputs.NO_CONSTR_TOTAL.value] = conf_total
ret[Outputs.DEV_FITNESS.value] = 1.0 - float(dev_total) / float(
conf_total) if conf_total > 0 else 1.0
ret[Outputs.DEVIATIONS.value] = sorted(ret[Outputs.DEVIATIONS.value],
key=lambda x: (x[0], x[1]))
ret[Outputs.IS_FIT.value] = len(ret[Outputs.DEVIATIONS.value]) == 0
return ret
def apply_actlist(trace, model, parameters=None):
"""
Apply log-skeleton based conformance checking given the list of activities of a trace
and a log-skeleton model
Parameters
--------------
trace
List of activities of a trace
model
Log-skeleton model
parameters
Parameters of the algorithm, including:
- the activity key (pm4py:param:activity_key)
- the list of considered constraints (considered_constraints) among: equivalence, always_after, always_before, never_together, directly_follows, activ_freq
Returns
--------------
aligned_trace
Containing:
- is_fit => boolean that tells if the trace is perfectly fit according to the model
- dev_fitness => deviation based fitness (between 0 and 1; the more the trace is near to 1 the more fit is)
- deviations => list of deviations in the model
"""
if parameters is None:
parameters = {}
consid_constraints = parameters[
CONSIDERED_CONSTRAINTS] if CONSIDERED_CONSTRAINTS in parameters else DEFAULT_CONSIDERED_CONSTRAINTS
trace_info = trace_skel.get_trace_info(trace)
ret = {}
ret["deviations"] = []
dev_total = 0
conf_total = 0
i = 0
while i < len(DEFAULT_CONSIDERED_CONSTRAINTS):
if DEFAULT_CONSIDERED_CONSTRAINTS[i] in consid_constraints:
if DEFAULT_CONSIDERED_CONSTRAINTS[i] == "activ_freq":
this_constraints = {
x: y
for x, y in model[
DEFAULT_CONSIDERED_CONSTRAINTS[i]].items()
}
conf_total += len(
list(act for act in trace_info[i]
if act in this_constraints)) + len(
list(act for act in trace_info[i]
if act not in this_constraints)) + len(
list(act for act in this_constraints
if min(this_constraints[act]) > 0
and not act in trace))
for act in trace_info[i]:
if act in this_constraints:
if trace_info[i][act] not in this_constraints[act]:
dev_total += 1
ret["deviations"].append(
(DEFAULT_CONSIDERED_CONSTRAINTS[i],
(act, trace_info[i][act])))
else:
dev_total += 1
ret["deviations"].append(
(DEFAULT_CONSIDERED_CONSTRAINTS[i], (act, 0)))
for act in this_constraints:
if min(this_constraints[act]) > 0 and not act in trace:
dev_total += 1
ret["deviations"].append(
(DEFAULT_CONSIDERED_CONSTRAINTS[i],
(act, this_constraints[act])))
elif DEFAULT_CONSIDERED_CONSTRAINTS[i] == "never_together":
this_constraints = {
x
for x in model[DEFAULT_CONSIDERED_CONSTRAINTS[i]]
if x[0] in trace
}
conf_total += len(this_constraints)
setinte = this_constraints.intersection(trace_info[i])
dev_total += len(setinte)
if len(setinte) > 0:
ret["deviations"].append(
(DEFAULT_CONSIDERED_CONSTRAINTS[i], tuple(setinte)))
else:
this_constraints = {
x
for x in model[DEFAULT_CONSIDERED_CONSTRAINTS[i]]
if x[0] in trace
}
conf_total += len(this_constraints)
setdiff = this_constraints.difference(trace_info[i])
dev_total += len(setdiff)
if len(setdiff) > 0:
ret["deviations"].append(
(DEFAULT_CONSIDERED_CONSTRAINTS[i], tuple(setdiff)))
i = i + 1
ret["no_dev_total"] = dev_total
ret["no_constr_total"] = conf_total
ret["dev_fitness"] = 1.0 - float(dev_total) / float(
conf_total) if conf_total > 0 else 1.0
ret["deviations"] = sorted(ret["deviations"], key=lambda x: (x[0], x[1]))
ret["is_fit"] = len(ret["deviations"]) == 0
return ret